A Smoothing Newton-Type Algorithm of Stronger Convergence for the Quadratically Constrained Convex Quadratic Programming
نویسندگان
چکیده
In this paper we propose a smoothing Newton-type algorithm for the problem of minimizing a convex quadratic function subject to finitely many convex quadratic inequality constraints. The algorithm is shown to converge globally and possess stronger local superlinear convergence. Preliminary numerical results are also reported.
منابع مشابه
Global convergence of an inexact interior-point method for convex quadratic symmetric cone programming
In this paper, we propose a feasible interior-point method for convex quadratic programming over symmetric cones. The proposed algorithm relaxes the accuracy requirements in the solution of the Newton equation system, by using an inexact Newton direction. Furthermore, we obtain an acceptable level of error in the inexact algorithm on convex quadratic symmetric cone programmin...
متن کاملA modified alternating direction method for convex quadratically constrained quadratic semidefinite programs
We propose a modified alternate direction method for solving convex quadratically constrained quadratic semidefinite optimization problems. The method is a first-order method, therefore requires much less computational effort per iteration than the second-order approaches such as the interior point methods or the smoothing Newton methods. In fact, only a single inexact metric projection onto th...
متن کاملInexact Josephy–Newton framework for variational problems and its applications to optimization
We propose and analyze a perturbed version of the classical Josephy-Newton method for solving generalized equations, and of the sequential quadratic programming method for optimization problems. This perturbed framework is convenient to treat in a unified way standard sequential quadratic programming, its stabilzed version [9, 2], sequential quadratically constrained quadratic programming [1, 4...
متن کاملA feasible second order bundle algorithm for nonsmooth, nonconvex optimization problems with inequality constraints: I. Derivation and convergence
This paper extends the SQP-approach of the well-known bundle-Newton method for nonsmooth unconstrained minimization to the nonlinearly constrained case. Instead of using a penalty function or a filter or an improvement function to deal with the presence of constraints, the search direction is determined by solving a convex quadratically constrained quadratic program to obtain good iteration poi...
متن کاملQuadratic Convergence of Newton’s Method for Convex Interpolation and Smoothing
In this paper, we prove that Newton’s method for convex best interpolation is locally q-quadratically convergent, giving an answer to a question of Irvine, Marine and Smith [7] and strengthening a result of Andersson and Elfving [1] and our previous work [5]. A damped Newton-type method is presented which has global q-quadratic convergence. Analogous results are obtained for the convex smoothin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Comp. Opt. and Appl.
دوره 35 شماره
صفحات -
تاریخ انتشار 2006